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Top-k Matching Queries for Filter-Based Profile Matching in Knowledge Bases

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9828)


Finding the best matching job offers for a candidate profile or, the best candidates profiles for a particular job offer, respectively constitutes the most common and most relevant type of queries in the Human Resources (HR) sector. This technically requires to investigate top-k queries on top of knowledge bases and relational databases. We propose in this paper a top-k query algorithm on relational databases able to produce effective and efficient results. The approach is to consider the partial order of matching relations between jobs and candidates profiles together with an efficient design of the data involved. In particular, the focus on a single relation, the matching relation, is crucial to achieve the expectations.


  • Relational Database
  • Matching Relation
  • Applicant Profile
  • Minimum Match
  • Profile Match

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The research reported in this paper was supported by the Austrian Forschungsförderungsgesellschaft (FFG) for the Bridge project “Accurate and Efficient Profile Matching in Knowledge Bases” (ACEPROM) under contract [FFG: 841284]. The research reported in this paper has been supported by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the Province of Upper Austria in the frame of the COMET center SCCH [FFG: 844597].

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  1. Chakrabarti, K., Ortega-Binderberger, M., Mehrotra, S., Porkaew, K.: Evaluating refined queries in top-k retrieval systems. IEEE Trans. Knowl. Data Eng. 16(2), 256–270 (2004)

    CrossRef  Google Scholar 

  2. Ilyas, I.F., Aref, W.G., Elmagarmid, A.K.: Supporting top-k join queries in relational databases. VLDB J. 13(3), 207–221 (2004)

    CrossRef  Google Scholar 

  3. Paoletti, A.L., Martinez-Gil, J., Schewe, K.-D.: Extending knowledge-based profile matching in the human resources domain. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds.) DEXA 2015. LNCS, vol. 9262, pp. 21–35. Springer, Heidelberg (2015)

    CrossRef  Google Scholar 

  4. Popov, N., Jebelean, T.: Semantic matching for job search engines: a logical approach. Technical report 13-02, RISC Report Series, University of Linz, Austria (2013)

    Google Scholar 

  5. Rácz, G., Sali, A., Schewe, K.-D.: Semantic matching strategies for job recruitment: a comparison of new and known approaches. In: Gyssens, M., Simari, G. (eds.) FoIKS 2016. LNCS, vol. 9616, pp. 149–168. Springer, Heidelberg (2016). doi:10.1007/978-3-319-30024-5_9

    CrossRef  Google Scholar 

  6. Straccia, U., Madrid, N.: A top-k query answering procedure for fuzzy logic programming. Fuzzy Sets Syst. 205, 1–29 (2012)

    MathSciNet  CrossRef  MATH  Google Scholar 

  7. Theobald, M., Weikum, G., Schenkel, R.: Top-k query evaluation with probabilistic guarantees. In: (e)Proceedings of the Thirtieth International Conference on Very Large Data Bases, Toronto, Canada, pp. 648–659, 31 August–3 September 2004

    Google Scholar 

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Correspondence to Alejandra Lorena Paoletti .

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Paoletti, A.L., Martinez-Gil, J., Schewe, KD. (2016). Top-k Matching Queries for Filter-Based Profile Matching in Knowledge Bases. In: Hartmann, S., Ma, H. (eds) Database and Expert Systems Applications. DEXA 2016. Lecture Notes in Computer Science(), vol 9828. Springer, Cham.

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